2018
Analysis of treatment pathways for three chronic diseases using OMOP CDM
Zhang X, Wang L, Miao S, Xu H, Yin Y, Zhu Y, Dai Z, Shan T, Jing S, Wang J, Zhang X, Huang Z, Wang Z, Guo J, Liu Y. Analysis of treatment pathways for three chronic diseases using OMOP CDM. Journal Of Medical Systems 2018, 42: 260. PMID: 30421323, PMCID: PMC6244882, DOI: 10.1007/s10916-018-1076-5.Peer-Reviewed Original ResearchConceptsTreatment pathwaysChronic diseasesStudy of drugsClinical data repositoryClinical treatmentDifferent medical institutionsProportion of monotherapyFirst-line medicationMedical institutionsFirst Affiliated HospitalType 2 diabetesNanjing Medical UniversityDifferent treatment pathwaysMost patientsCommon medicationsAffiliated HospitalMedicationsNational guidelinesMedication informationLocal hospitalMedical UniversitySame diseaseDiseasePatientsNew drugs
2013
Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy A, Abramson V, Bhave S, Levy M, Xu H, Yankeelov T. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. Journal Of The American Medical Informatics Association 2013, 20: 688-695. PMID: 23616206, PMCID: PMC3721158, DOI: 10.1136/amiajnl-2012-001332.Peer-Reviewed Original ResearchConceptsNeoadjuvant chemotherapyFeature selectionCycles of NACPredictive model buildingTime most patientsBreast cancer patientsImportant clinical problemCourse of therapyMachine learningDynamic contrast-enhanced MRIContrast-enhanced MRIQuantitative dynamic contrast-enhanced MRIMost patientsTreatment regimenCancer patientsClinical variablesTherapeutic responseBreast cancerPredictive modeling approachClinical problemData show promiseLogistic regressionPatientsMachineDiffusion-weighted MRI data